Machine learning-aided latency prediction in packet-switched xhaul networks
In this work, we address the challenge of accurately predicting latency in packet-switched Xhaul networks, enabling the convergent transport of fronthaul (FH) and midhaul (MH) traffic within radio access networks (RANs). Although deterministic worst-case (WC) models provide strict latency bounds, th...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:upcommonspor::3d43771af75120967ed51da518571094 |
| Acceso en línea: | https://hdl.handle.net/2117/461101 https://dx.doi.org/10.1109/ACCESS.2026.3678383 |
| Access Level: | acceso abierto |
| Palabra clave: | 5G mobile communication Open RAN Optical wavelength conversion Delays Predictive models Estimation Optimization 6G mobile communication Reliability Routing Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Comunicacions mòbils Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | In this work, we address the challenge of accurately predicting latency in packet-switched Xhaul networks, enabling the convergent transport of fronthaul (FH) and midhaul (MH) traffic within radio access networks (RANs). Although deterministic worst-case (WC) models provide strict latency bounds, they tend to significantly overestimate actual flow latencies, leading to inefficient resource allocation. To address this limitation, we propose a machine learning-based (ML) latency prediction framework that leverages quantile regression (QR) to provide more accurate estimates of maximum one-way transmission latency for both FH and MH flows — an essential requirement for reliable RAN operation. Our approach enhances WC estimations by incorporating additional latency-related features and is validated using an extensive dataset generated from simulations of diverse ring and mesh topologies. We integrate the QR-based latency predictions into a mixed-integer linear programming (MILP) model for optimal flow routing and distributed unit (DU) placement. A comparative analysis reveals that QR-based latency prediction outperforms WC latency estimations, significantly improving network performance by reducing the number of active DU processing nodes by up to 20% without compromising latency constraints. The results highlight the potential of ML techniques to enhance the accuracy of latency modeling in dynamic, latency-sensitive Xhaul scenarios, thereby contributing to the realization of RAN Digital Twin systems envisioned for future 6G networks. |
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